The overarching objective of this paper is to introduce a novel Fast, Efficient, and Scalable k-means k-means (FES-k-means*) algorithm. This algorithm is designed to increase the overall performance of the standard k-means clustering technique. The FES-k-means* algorithm uses a hybrid approach that comprises the k-d tree data structure, the nearest neighbor query, the standard k-means algorithm, and Mashor’s adaptation rate. The algorithm is tested using two real datasets and two synthetic datasets and is employed twice on all four datasets. The first trial consisted of previously MIL-SOM* trained data, and the second was on raw, untrained data. The approach presented with this method enables unfounded knowledge discovery, otherwise unclaimed by conventional clustering methods. When used in conjunction with the MIL-SOM* training technique, theFES-k-means* algorithm reduces the computation time and produces quality clusters. In particular, the robust FES-k-means* method opens doors to (1) faster cluster production than conventional clustering methods, (2) scalability allowing application in other platforms, and its ability to handle small and large datasets, compact or scattered, and (3) efficient geospatial data analysis of large datasets. All of the above makes FES-k-means* live up to defending its well-deserved name—Fast, Efficient, and Scalable k-means (FES-k-means*). The findings of this study are vital to the relatively new and expanding subfield of geospatial data management.
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Oyana, T.J., Scott, K.E. (2008). A Geospatial Implementation of a Novel Delineation Clustering Algorithm Employing the K-means . In: Bernard, L., Friis-Christensen, A., Pundt, H. (eds) The European Information Society. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78946-8_8
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